Introduction to Neural Networks

Introduction to Neural Networks

This course is part of Foundations of Neural Networks Specialization

Instructor: Zerotti Woods

What you'll learn

Understand the foundational mathematics and key concepts driving neural networks and machine learning.
Analyze and apply machine learning algorithms, optimization methods, and loss functions to train and evaluate models effectively.
Explore the design and structure of feedforward neural networks, using gradient descent to optimize and train deep models.
Investigate convolutional neural networks, their elements, and how they apply to real-world problems like image processing and computer vision.

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Assessments

10 assignments

Taught in English

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This course is part of the Foundations of Neural Networks Specialization

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There are 6 modules in this course

The course "Introduction to Neural Networks" provides a comprehensive introduction to the foundational concepts of neural networks, equipping learners with essential skills in deep learning and machine learning. Dive into the mathematics that drive neural network algorithms and explore the optimization techniques that enhance their performance. Gain hands-on experience training machine learning models using gradient descent and evaluate their effectiveness in practical scenarios.

You’ll also delve into the architecture of feedforward neural networks and the innovative techniques used to prevent overfitting, such as dropout and regularization. The course uniquely emphasizes Convolutional Neural Networks (CNNs), highlighting their applications in fields like computer vision and image processing. Real-world examples and research insights will help you stay current with advancements in neural networks while preparing you to propose innovative solutions for emerging challenges. This course offers the tools and knowledge to advance your expertise in algorithms and machine learning methodologies.

This course provides a comprehensive overview of the foundational mathematics and concepts behind Deep Learning and Machine Learning. Students will analyze various Machine Learning Algorithms, focusing on Optimization Techniques and Regularization Methods, while evaluating their effectiveness. Practical applications will include training algorithms using Gradient Descent and assessing their performance. The course also covers the structure and data elements of Convolutional Neural Networks (CNNs), emphasizing their design for specific tasks. Lastly, students will explore current research and propose future directions in Regularization and CNNs, contributing to advancements in Deep Learning methodologies.

What's included

2 readings

This module will lay the foundations that are needed to be successful in the field of Deep Learning. It will also introduce motivation for the field as well as discuss the history of the field.

What's included

3 videos1 reading2 assignments1 ungraded lab

3 videosTotal 43 minutes

Introduction and Background15 minutesPreview module
History, Overview, and Foundations11 minutes
Foundational Mathematics for Deep Learning15 minutes

1 readingTotal 60 minutes

Reading References60 minutes

2 assignmentsTotal 75 minutes

Overview and Foundations60 minutes
History and Overview15 minutes

1 ungraded labTotal 60 minutes

Hands-On Lab: Closest and Furthest Points on a Circle60 minutes

This module will discuss the fundamentals of Machine Learning. You will explore different aspects of Machine Learning Algorithms and what is needed to create an algorithm.

What's included

1 video1 reading2 assignments1 ungraded lab

This module will discuss the building blocks of Deep Feedforward Neural Networks. Students will explore different parts of Deep Feedforward NN and what is needed to create and train the algorithms.

What's included

1 video1 reading2 assignments1 ungraded lab

This module will discuss the regularization in Deep Feedforward Neural Networks. Learners will explore the reasons for regularization along with different techniques.